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dc.contributor.advisorRahman, Rafeed
dc.contributor.authorSiddique, Ahmed Zarir
dc.contributor.authorAli, Asif
dc.contributor.authorArefin, Mohammad Sultanul
dc.date.accessioned2025-01-15T04:35:40Z
dc.date.available2025-01-15T04:35:40Z
dc.date.copyright©2024
dc.date.issued2024-10
dc.identifier.otherID 20301409
dc.identifier.otherID 20201049
dc.identifier.otherID 20201138
dc.identifier.urihttp://hdl.handle.net/10361/25169
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 28-29).
dc.description.abstractEver since artificial intelligence was discovered, numerous research endeavours have concentrated on comprehending its significance inside the corporate environment [13]. These days customers are more into the quality of services provided by organisations [5]. The growing number of organizations resulted in an increase in competition and customer retention has become a major factor for businesses, as understanding its influence can aid companies to develop effective marketing strategies. The purpose of this paper is to comprehensively be able to understand the e-commerce dynamics and use relevant machine learning techniques to evaluate and use the results for the prediction of customer loyalty. The paper discusses the analysis of customer loyalty using various data mining techniques, such as decision tree, SVM, random forest, and logistic regression etc. We constructed some simple ensemble applications using the machine learning algorithms, with the dataset that we received from a Bangladesh-based e-commerce business. In the end, the abovementioned algorithms are all carried out and the result demonstrates which model is best for retaining customer loyalty.en_US
dc.description.statementofresponsibilityAhmed Zarir Siddique
dc.description.statementofresponsibilityAsif Ali
dc.description.statementofresponsibilityMohammad Sultanul Arefin
dc.format.extent38 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectE-commerceen_US
dc.subjectCustomer relationship managementen_US
dc.subjectCustomer loyaltyen_US
dc.subjectMachine learning
dc.subject.lcshArtificial intelligence.
dc.subject.lcshElectronic commerce--Databases.
dc.subject.lcshData mining.
dc.titlePredictive models for customer retention in Bangladesh: enabling proactive strategiesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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